A Robust GLRT Detector Against Missing Data in Cooperative Sensing

Abstract

Cooperative sensing, a technique employed in cognitive radio (CR) networks for spectrum sensing, exhibits promising potential in bolstering spectrum utilization and enhancing network performance. This approach leverages the information captured by distributed CR users, which is subsequently aggregated at a fusion center. However, the challenges arise when the data are transmitted with low-quality, resulting in the consequential issue of missing data. These factors introduce complexity in detecting primary signals and undermine the reliability of cooperative sensing. In this study, we present a significant advancement in cooperative sensing methodologies by introducing a novel approach: a generalized likelihood ratio test (GLRT) type detector specifically designed to be robust to missing data. More specifically, our proposed robust GLRT detector modifies the computation of the classical GLRT test statistic to accommodate the inherent incompleteness of the data and effectively estimates the desired unknown parameters. Through numerical experiments, we demonstrate the resilience and robustness of our proposed cooperative signal detection method.